AAIA Practice Exam 3 — 20 Questions

Question 1

Which component of an AI policy is most important for establishing organizational accountability?

Accepted Answer

Defining the roles and responsibilities of the AI Governance Committee and model owners. Accountability requires clearly defined roles and responsibilities for AI system oversight.

Answer

Describing the history of artificial intelligence research

Answer

Listing the hardware specifications of the training clusters

Answer

Providing the contact information for the company's PR department

Question 2

When auditing model versioning, what is the recommended practice for maintaining evidence?

Accepted Answer

Use immutable versioning. Immutable model repositories and versioning control (e.g., Git LFS, MLflow) ensure the exact state can be audited.

Answer

Delete old versions

Answer

Use mutable file names

Answer

Store models in cache

Question 3

Which TWO monitoring tools are typically used to detect 'Data Drift' in production pipelines?

Accepted Answer

AWS SageMaker Model Monitor. AWS SageMaker Model Monitor and Google Cloud Vertex AI Model Monitoring are industry standard tools for drift detection.

Answer

Web Browser

Answer

Microsoft Excel

Answer

Adobe Photoshop

Question 4

A board of directors asks you to define the 'AI Risk Appetite'. Which approach best satisfies the governance requirement?

Accepted Answer

Defining quantitative thresholds for acceptable model performance and ethical impact. Risk appetite must align with the organization's business objectives and ethical standards.

Answer

Creating a repository of all AI ethical guidelines published by NGOs

Answer

Providing the board with a list of all AI vendors used in the organization

Answer

Restricting all AI development to on-premises environments

Question 5

When scoping an AI audit engagement, which TWO of the following documents should the auditor request to understand the AI model's governance structure?

Accepted Answer

Technical system architecture documentation. Model Cards and technical documentation provide the necessary governance and implementation context for an auditor.

Answer

Public social media posts

Answer

Company marketing brochures

Answer

Developer salary structures

Question 6

An auditor is evaluating the data pipeline security. Which THREE controls should be verified?

Accepted Answer

Full audit log of data provenance. Access control, encryption at rest, and data provenance are standard security controls for AI data pipelines.

Answer

The number of lines of code in the pipeline

Answer

Office access badge logs

Question 7

A firm uses a third-party AI service. Which control is most critical to ensure compliance with the firm's own AI governance policy?

Accepted Answer

Including AI-specific compliance requirements in the vendor service-level agreement (SLA). Supply chain risk management requires mapping vendor controls to internal governance requirements.

Answer

Conducting weekly penetration tests on the vendor's office premises

Answer

Requesting the vendor's employee turnover rates for the last fiscal year

Answer

Requiring the vendor to submit copies of all their marketing brochures

Question 8

What is the purpose of 'Model Versioning' in an AI audit context?

Accepted Answer

To allow for reproducibility and auditability of past model versions. Versioning ensures that the specific state of a model used for a specific decision can be retrieved and audited.

Answer

To automatically delete old models to save space

Answer

To hide the model code from competitors

Answer

To reduce the number of files on the server

Question 9

Which technique is most appropriate for mitigating 'Concept Drift' in a production model?

Accepted Answer

Implementing an automated re-training pipeline based on performance threshold triggers. Continuous re-training or fine-tuning based on recent data is the standard mitigation for concept drift.

Answer

Increasing the number of neurons in the hidden layers

Answer

Moving the model to a larger GPU cluster

Answer

Restricting access to the training dataset

Question 10

Which TWO of the following are core components of an AI incident response strategy?

Accepted Answer

Defined communication protocols for internal and external stakeholders. Incident response requires both technical preparation and clear communication channels to handle potential AI failures.

Answer

Marketing campaigns for AI product launches

Answer

Software license management for all developers

Answer

Automated email server maintenance

Question 11

Which TWO metrics provide the best insights into 'Model Quality' during an audit?

Accepted Answer

Recall score. Precision and Recall are fundamental metrics for assessing the quality of classification models.

Answer

Disk space usage

Answer

Number of server restarts

Answer

Team's average coffee intake

Question 12

Which TWO of the following are examples of 'Data Leakage' that an auditor should look for in a pipeline?

Accepted Answer

Training on test data. Target leakage and future data leakage are primary types of data leakage in AI pipelines.

Answer

Using cloud-based storage

Answer

Documenting model parameters

Answer

Using open-source libraries

Question 13

When auditing an AI system for regulatory compliance, you find that data lineage is broken between the feature store and the training pipeline. Which control is most likely deficient?

Accepted Answer

Metadata logging and transformation documentation. Feature stores must maintain end-to-end traceability to ensure that data provenance is clear and auditable.

Answer

Access control lists for the model production environment

Answer

Encryption of data at rest in the data warehouse

Answer

Load balancing configuration for the inference API

Question 14

Which TWO of the following are primary components of an AI model risk management inventory?

Accepted Answer

Training data provenance and lineage information. An inventory must track metadata and lifecycle status for accountability.

Answer

Employee performance review scores

Answer

List of office furniture used by the team

Answer

Marketing budget for the model launch

Question 15

Which environment should be used to perform 'Model Validation' before moving to production?

Accepted Answer

Staging Environment. Staging or UAT environments mirror production and are used for testing.

Answer

Training Cluster

Answer

Development Environment

Answer

Public Internet

Answer

Local Desktop

Question 16

Which metric would best detect 'Label Drift' in a production model?

Accepted Answer

Target distribution shift (PSI). Label drift occurs when the output (target) distribution changes significantly from training.

Answer

Model latency

Answer

CPU utilization

Answer

Number of users

Question 17

What is the most effective way to address 'Adversarial Input' during inference?

Accepted Answer

Adversarial training and input filtering. Implementing input validation/sanitization and adversarial training (training on adversarial examples) improves robustness.

Answer

Reducing training time

Answer

Increasing model depth

Answer

Ignoring the input

Question 18

Which THREE items should be tracked in a 'Model Registry' for audit readiness?

Accepted Answer

Training dataset lineage. Registration must link code, data, and model artifacts.

Answer

Cloud provider logo

Answer

Employee birthday

Question 19

During an AI risk assessment, you notice that the 'Model Risk Management' (MRM) framework does not mandate independent validation for models categorized as 'High Impact'. What is the most significant consequence?

Accepted Answer

Increased probability of undetected systemic model bias or failure. Independent validation is a core pillar of model risk governance, especially for high-impact systems.

Answer

Inability to detect model drift in production environments

Answer

Non-compliance with internal cybersecurity patching cycles

Answer

Operational silos between the AI team and the IT department

Question 20

What is the goal of a 'Human-in-the-loop' (HITL) control in an AI system?

Accepted Answer

To provide human oversight for automated decisions to mitigate risk and error. HITL ensures that a human makes final decisions for high-stakes AI outputs, reducing the risk of automation error.

Answer

To reduce the time spent on manual data entry

Answer

To replace all human employees with automated systems

Answer

To increase the speed of model training